pyhf.simplemodels.uncorrelated_background#

pyhf.simplemodels.uncorrelated_background(signal, bkg, bkg_uncertainty, batch_size=None, validate=True, poi_name='mu')[source]#

Construct a simple single channel Model with a shapesys modifier representing an uncorrelated background uncertainty.

Example

>>> import pyhf
>>> pyhf.set_backend("numpy")
>>> model = pyhf.simplemodels.uncorrelated_background(
...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
... )
>>> model.schema
'model.json'
>>> model.config.channels
['singlechannel']
>>> model.config.samples
['background', 'signal']
>>> model.config.parameters
['mu', 'uncorr_bkguncrt']
>>> model.expected_data(model.config.suggested_init())
array([ 62.        ,  63.        , 277.77777778,  55.18367347])

Parameters:

signal (list) – The data in the signal sample
bkg (list) – The data in the background sample
bkg_uncertainty (list) – The statistical uncertainty on the background sample counts
batch_size (None or int) – Number of simultaneous (batched) Models to compute
validate (bool) – If True, validate the model before returning. Only set this to False if you have an experimental use case and know what you’re doing.
poi_name (str) – The Model parameter of interest name. Defaults to "mu".

Returns:

The statistical model adhering to the model.json schema

Return type:

Model

Changed in version 0.8.0: Added poi_name argument.